Sean Thompson

Bio: Sean Thompson is an academic researcher from University of Toronto. The author has an hindex of 1, co-authored 1 publications receiving 76 citations.

Quantitative Textural Measurements in Igneous and Metamorphic Petrology

Abstract: Acknowledgements 1. Introduction 1.1 Petrological methods 1.2 Qualitative versus quantitative data 1.3 What do I mean by texture? 1.4 Information density and data sources 1.5 Structure of this book 1.6 Software applications for quantitative textural studies 2. General analytical methods 2.1 Introduction 2.2 Complete three dimensional analytical methods 2.3 Extraction of grain parameters from data volumes 2.4 Destructive partial analytical methods 2.5 Surface and section analytical methods 2.6 Extraction of textural parameters from images 2.7 Calculation of three dimensional data from two dimensional observations 2.8 Verification of theoretical parameter distributions 2.9 Summary 3. Grain and crystal sizes 3.1 Introduction 3.2 Review of theory 3.3 Analytical methods 3.4 Typical applications 4. Grain shape 4.1 Introduction 4.2 Brief review of theory 4.3 Methodology 4.4 Typical applications 5. Grain orientations - rock fabric 5.1 Introduction 5.2 Brief review of theory 5.3 Introduction to fabric methodology 5.4 Determination of shape preferred orientations 5.5 Determination of lattice preferred orientations 5.6 3D bulk fabric methods - combined SPO and LPO 5.7 Extraction of grain orientation data and parameters 5.8 Typical applications 6. Grain spatial distributions and relations 6.1 Introduction 6.2 Brief review of theory 6.3 Methodology 6.4 Typical applications 7. Textures of fluid-filled pores 7.1 Introduction 7.2 Brief review of theory 7.3 Methodology 7.4 Parameter values and display 7.5 Typical applications 8. Appendix. Computer programs for use in quantitative textural analysis - freeware, shareware and commercial 8.1 Abbreviations 8.2 General analytical methods 8.3 Grain and crystal sizes 8.4 Grain shape 8.5 Grain orientations - rock fabric 8.6 Grain spatial distributions and relations Figure captions References.

Machine Learning of Mineralization-Related Geochemical Anomalies: A Review of Potential Methods

Abstract: Research on processing geochemical data and identifying geochemical anomalies has made important progress in recent decades Fractal/multi-fractal models, compositional data analysis, and machine learning (ML) are three widely used techniques in the field of geochemical data processing In recent years, ML has been applied to model the complex and unknown multivariate geochemical distribution and extract meaningful elemental associations related to mineralization or environmental pollution It is expected that ML will have a more significant role in geochemical mapping with the development of big data science and artificial intelligence in the near future In this study, state-of-the-art applications of ML in identifying geochemical anomalies were reviewed, and the advantages and disadvantages of ML for geochemical prospecting were investigated More applications are needed to demonstrate the advantage of ML in solving complex problems in the geosciences